Matemática e Estatística | Comunicações em congressos, conferências e seminários / Communications in congresses, conferences
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- Multivariable fuzzy control for the simultaneous administration of the anaesthetic and the analgesic drugsPublication . Nunes, Catarina S.; Mahfouf, M.; Linkens, D. A.; Peacock, J.A multivariable fuzzy controller developed for the simultaneous administration of the anaesthetic drug (propofol) and the analgesic drug (remifentanil), is presented. The controller was designed in order to achieve a steady state level of depth of anaesthesia (DOA) and to reduce the amount of drug infused. The multivariable fuzzy controller is based on linguistic rules that interact with three decision tables, one of which represents a fuzzy PI controller. Optimisation using genetic algorithms was used to determine the scaling factors of the fuzzy PI controller. According to the different possibilities for the DOA level and for the surgical stimuli, the multivariable controller defines the required change in the infusion rates of the two drugs. A patient model of the interaction between propofol and remifentanil was used to test the multivariable controller. The controller was able to adjust the remifentanil infusion rate according to the stimulus intensity, and takes advantage of the synergistic interaction to the change adequately the propofol infusion rate. Propofol is titrated to lower infusion rates, decreasing the amount of drug infused, and speeding up recovery. In addition, the controller ensures adequate analgesia by titrating the remifentanil according to stimulus. The multivariable fuzzy controller was tested under different simulations, and responded efficiently to different induction profiles, set point changes and disturbances.
- Radial basis function neural networks versus fuzzy models to predict return of consciousness after general anesthesiaPublication . Nunes, Catarina S.; Mendonca, T. F.; Amorim, Pedro; Ferreira, D. A.; Antunes, L. M.This paper presents two modelling techniques to predict return of consciousness (ROC) after general anaesthesia, considering the effect concentration of the anaesthetic drug at awakening. First, several clinical variables were statistically analysed to determine their correlation with the awakening concentration. The anaesthetic and the analgesic mean dose during surgery, and the age of the patient, proved to have significantly high correlation coefficients. Variables like the mean bispectral index value during surgery, duration of surgery did not present a statistical relation with ROC. Radial basis function (RBF) neural networks were trained relating different sets of clinical values with the anaesthetic drug effect concentration at awakening. Secondly, fuzzy models were built using an Adaptive Network-Based Fuzzy Inference System (ANFIS) also relating different sets of variables. Clinical data was used to train and test the models. The fuzzy models and RBF neural networks proved to have good prediction properties and balanced results.
- Comparison of neural networks, fuzzy and stochastic prediction models for return of consciousness after general anesthesiaPublication . Nunes, Catarina S.; Mendonca, T. F.; Amorim, Pedro; Ferreira, D. A.; Antunes, L.This paper presents three modeling techniques to predict return of consciousness (ROC) after general anesthesia, considering the effect concentration of the anesthetic drug at awakening. First, several clinical variables were statistically analysed to determine their correlation with the awakening concentration. The anesthetic and the analgesic mean dose during surgery, and the age of the patient, proved to have significantly high correlation coefficients. Variables like the mean bispectral index value during surgery, duration of surgery did not present a statistical relation with ROC. Stochastic regression models were built using the variables with higher correlation. Secondly, fuzzy models were built using an Adaptive Network-Based Fuzzy Inference System (ANFIS) also relating different sets of variables. Thirdly, radial basis function (RBF) neural networks were trained relating different sets of clinical values with the anesthetic drug effect concentration at awakening. Clinical data was used to train and test the models. The stochastic models and the fuzzy models proved to have good prediction properties. The RBF network models were more biased towards the training set. The best balanced performance was achieved with the fuzzy models.
- Predictive adaptive control of unconsciousness: exploiting remifentanil as an accessible disturbancePublication . Mendonca, Teresa; Nunes, Catarina S.; Magalhaes, Hugo; Lemos, Joao M.; Amorim, PedroThe problem of controlling the level of unconsciousness measured by the BIS index of patients under anesthesia, is considered. It is assumed that the manipulated variable is the administration rate of propofol, while remifentanil is also administered for analgesia. Since these two drugs interact, the administration rate of remifentanil is considered as an accessible disturbance. A predictive adaptive controller structure that explores this fact is proposed and illustrated by means of simulation.
- EEG entropy monitoring of depth of anaesthesia: pharmacokinetic and dynamic modellingPublication . Castro, Ana; Bressan, Nadja; Antunes, Luís; Nunes, Catarina S.Because of the difficulty in analyzing raw electroencephalographic signal, several electroencephalographic monitors have been developed to aid anaesthetists on their task to maintain adequate anaesthesia. Spectral Entropy is used as a measure of electroencefalographic effects of drugs in human patients, and is a valuable tool to predict depth of anaesthesia. Monitors with implemented entropy algorithms, process the electroencephalogram (EEG) and are in current use at the operating room. In this study we used the EEG collected in rats and applied the Shannon entropy over the signal. The information obtained was used as an indicator of depth of anaesthesia. The main objective was to model the relation between the depth of anaesthesia in rats (entropy) and the propofol infusion rates, with the purpose of obtaining a closed-loop control for propofol infusions. Five adult rats were sedated with isoflurane, cannulated and equipped for the EEG collection. After the preparation, anaesthesia was induced with propofol infusions, using different infusion rates on each rat. The collected EEG (125Hz) was processed using an entropy algorithm developed in MATLAB R 7 that determined the entropy value at each second using the preceding 15s of signal. Pharmacokinetic models were fitted for each rat using bi and tri-compartmental models; the pharmacodynamic phase was also modelled for each rat. The relation between obtained propofol effect-site concentrations and the entropy values was modelled by a Hill Equation. The model obtained for the relation between infusion rates and entropy values was evaluated using the mean absolute deviation (MAD) and the relative mean square error (RMSE) for models comparative analysis.
- Predictive adaptive control of the bispectral index of the EEG (BIS): exploring electromyography as an accessible disturbancePublication . Nunes, Catarina S.; Mendonca, T.; Lemos, J. M.; Amorim, PedroThe problem of controlling the level of unconsciousness measured by the Bispectral Index of the EEG (BIS) of patients under anaesthesia, is considered. It is assumed that the manipulated variable is the infusion rate of the hypnotic drug propofol, while the drug remifentanil is also administered for analgesia. Since electromyography(EMG) interferes with the BIS signal, it is considered as an accessible disturbance. In order to tackle the high uncertain present on the system, the predictive adaptive controller MUS MAR is used. The performance of the controller is illustrated by means of simulation with 45 patient individual adjusted models, which incorporate the effect of the drugs interaction on BIS. This controller structure proved to be robust to the EMG disturbance, changing reference values and noise.
- Towards the control of depth of anaesthesia: identification of patient variabilityPublication . Nunes, Catarina S.; Alonso, Hugo; Castro, Ana; Amorim, Pedro; Mendonca, TeresaDepth of anaesthesia (DOA) is usually assessed through the Bispectral Index (BIS) and State Entropy (SE), which derived EEG signals. Studying the effect of drug interaction on these signals is of great importance for the development of a suitable drug infusion system designed to control DOA. In this paper, two renowned pharmacokinetic (PK) models for the anaesthetic drug propofol are considered, and their influence on the fitting and prediction abilities of a drug interaction model for BIS and SE is assessed. This interaction model is fitted to the individual patient data during anaesthesia induction and tested for prediction during surgery. Two identification methods are considered for the fitting purpose: a hybrid method and a nonlinear least squares curve-fitting algorithm. The results obtained for 7 patients show that the choice of the PK model has influence on the overall performance of the interaction model; in particular, only one PK model leads to good results in the prediction phase. The choice of the identification method is equally important, being the hybrid method the better suited. The successful identification of patient variability here obtained is a key step towards the control of DOA.
- Convergence to self-similarity in an addition model with power-like time-dependent input of monomersPublication . Costa, Fernando Pestana da; Sasportes, Rafael; Pinto, João TeixeiraIn this note we extend the results published in Ref. 1 to a coagulation system with Becker-Doring type interactions and time-dependent input of monomers $J_{1}(t)$ of power–like type: $J_{1}(t)/(\alpha t^{\omega }) \rightarrow 1$ as $t \rightarrow \infty$, with $\alpha > 0$ and $\omega > − \frac{1}{2}$. The general framework of the proof follows Ref. 1 but a different strategy is needed at a number of points.
- Modelling the dynamics of depth of anaesthesia: cerebral state index in dogsPublication . Bressan, Nadja; Castro, A.; Bras, S.; Ribeiro, L.; Ferreira, D. A.; Silva, A.; Antunes, L.; Nunes, Catarina S.The goal of this study was to obtain models that described the relation between the anaesthetic drug infusions (propofol) and an electroencephalogram (EEG) derived index (Cerebral State Index - CSI) during general anaesthesia in dogs. The first phase integrated the adaptation of hardware for EEG acquisition and exploration for the best electrodes position in dogs skull. The clinical protocol implementation and data collection were the next steps followed by CSI modeling. CSI showed adequate response to changes in drug infusion, reflecting the changes of depth of anaesthesia in dogs. The models obtained adjusted well to the original CSI data and also predicted the CSI trend during surgery. Using this monitor in current practice might improve quality in the anaesthesia procedure providing a useful tool to administer a correct sedation.
- Nonlinear modeling of cerebral state index in dogsPublication . Bras, Susana; Bressan, Nadja; Ribeiro, Lenio; Ferreira, David A.; Antunes, Luis; Nunes, Catarina S.The Cerebral State Index (CSI) is an electroencephalogram derived signal representing the depression of central nervous activity produced by anesthetic drugs. In this study, a nonlinear model was developed to describe the CSI tendency during general anesthesia in dogs, by evaluating the effect of the anesthetic drug propofol. The model was based on a compartmental and Hill Equation structure with individually identified parameters. The clinical data of 14 dog surgeries were collected and used for modeling and testing. The model presented good results, following the CSI trend. A model for drug-effect for veterinarian anesthesia is an important step when developing advisory, educational and control systems. The overall aim is to improve animal safety and comfort.